Beyond performance metrics: Examining a decrease in students’ physics self-efficacy through a social networks lens

Dou, Remy; Brewe, Eric; Zwolak, Justyna P.; Potvin, Geoff; Williams, Eric A.; Kramer, L. H.

doi:10.1103/physrevphyseducres.12.020124

Cited by 78 publications

(78 citation statements)

References 46 publications

(90 reference statements)

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“…The most common method for addressing missing data in PER is to use listwise deletion to only analyze the complete cases, discarding data from any student who did not provide both the pretest and post-test; though, we know of at least one study in PER that used MI [32]. We used MI because it has the same basic assumptions of listwise deletion but it reduces the rate of type I error by using all the available information to better account for missing data [33].…”

Section: A Data Collection and Processingmentioning

confidence: 99%

Comparison of normalized gain and Cohen’sdfor analyzing gains on concept inventories

Nissen

Talbot

Thompson

et al. 2018

Phys. Rev. Phys. Educ. Res.

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Measuring student learning is a complicated but necessary task for understanding the effectiveness of instruction and issues of equity in college science, technology, engineering, and mathematics (STEM) courses. Our investigation focused on the implications on claims about student learning that result from choosing between one of two commonly used metrics for analyzing shifts in concept inventories. The metrics are normalized gain (g), which is the most common method used in physics education research and other discipline based education research fields, and Cohen's d, which is broadly used in education research and many other fields. Data for the analyses came from the Learning About STEM Student Outcomes (LASSO) database and included test scores from 4551 students on physics, chemistry, biology, and math concept inventories from 89 courses at 17 institutions from across the United States. We compared the two metrics across all the concept inventories. The results showed that the two metrics lead to different inferences about student learning and equity due to the finding that g is biased in favor of high pretest populations. We discuss recommendations for the analysis and reporting of findings on student learning data.

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Section: A Data Collection and Processingmentioning

confidence: 99%

Comparison of normalized gain and Cohen’sdfor analyzing gains on concept inventories

Nissen

Talbot

Thompson

et al. 2018

Phys. Rev. Phys. Educ. Res.

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“…For over half a decade we have applied SNA in the field of physics education research. That work has led to greater understandings of student persistence, changes in their career-related beliefs (e.g., physics interest), and their academic success [7,8,[12][13][14]. In the process, we also established SNA study design in the classroom arXiv:1809.00337v1 [physics.ed-ph] 2 Sep 2018 context, including development of data collection instruments, setup of protocols for gathering and digitizing data, as well as identification of network methodologies relevant to a wide range of research questions.…”

Section: Introductionmentioning

confidence: 99%

“…Learning is perceived as a construction of mutual understandings within a social context, with emphasis placed on examining discourse and interactions rather than "acquisition" of knowledge as a commodity or object [17]. As such, we espouse the philosophy that "learning and social interactions are not mutually exclusive" [12].…”

Section: Anxiety In the Introductory Physics Classroommentioning

confidence: 99%

Practitioner’s guide to social network analysis: Examining physics anxiety in an active-learning setting

Dou

Zwolak

2019

Phys. Rev. Phys. Educ. Res.

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The application of social network analysis (SNA) has recently grown prevalent in science, technology, engineering, and mathematics education research. Research on classroom networks has led to greater understandings of student persistence in physics majors, changes in their career-related beliefs (e.g., physics interest), and their academic success. In this paper, we aim to provide a practitioner's guide to carrying out research using SNA, including how to develop data collection instruments, setup protocols for gathering data, as well as identify network methodologies relevant to a wide range of research questions beyond what one might find in a typical primer. We illustrate these techniques using student anxiety data from active-learning physics classrooms. We explore the relationship between students' physics anxiety and the social networks they participate in throughout the course of a semester. We find that students' with greater numbers of outgoing interactions are more likely to experience negative anxiety shifts even while we control for pre anxiety, gender, and final course grade. We also explore the evolution of student networks and find that the second half of the semester is a critical period for participating in interactions associated with decreased physics anxiety. Our study further supports the benefits of dynamic group formation strategies that give students an opportunity to interact with as many peers as possible throughout a semester. To complement our guide to SNA in education research, we also provide a set of tools for letting other researchers use this approach in their work -the SNA toolbox -that can be accessed on GitHub.

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“…In addition, several studies have used SNA to quantify interactions in order to explore their impact on other constructs relevant to learning (e.g., course grades [7], self-efficacy [8], persistence [9]). …”

Section: Social Network Analysis (Sna)mentioning

confidence: 99%

“…In most of these studies [5,[7][8][9], ties were identified using self-reported data from a survey where participants name individuals with whom they interact. We propose an application of SNA where ties are instead extracted from qualitative analysis of classroom video data.…”

Section: Social Network Analysis (Sna)mentioning

confidence: 99%

Using Social Network Analysis on classroom video data

Pomian¹,

Zwolak²,

Sayre³

et al. 2018

2017 Physics Education Research Conference Proceedings

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We propose a novel application of Social Network Analysis (SNA) using classroom video data as a means of quantitatively and visually exploring the collaborations between students. The context for our study was a summer program that works with first generation students and deaf/hard-of-hearing students to engage in authentic science practice and develop a supportive community. We applied SNA to data from one activity during the two-week program to test our approach and as a means to begin to assess whether the goals of the program are being met. We used SNA to identify groups that were interacting in unexpected ways and then to highlight how individuals were contributing to the overall group behavior. We plan to expand our new use of SNA to video data on a larger scale.

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Beyond performance metrics: Examining a decrease in students’ physics self-efficacy through a social networks lens

Cited by 78 publications

References 46 publications

Comparison of normalized gain and Cohen’sdfor analyzing gains on concept inventories

Comparison of normalized gain and Cohen’sdfor analyzing gains on concept inventories

Practitioner’s guide to social network analysis: Examining physics anxiety in an active-learning setting

Using Social Network Analysis on classroom video data

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